First, following the related works, CASIA-MFSD (C) [1]} and ReplayAttack (I) [2]} are used for cross-dataset experiments, and the results are measured in HTER. During training, the numbers of fine-grained patch-type classes are 9 and 4 in \(C \rightarrow I\)  and \(I \rightarrow C\)  protocols, respectively. The results are shown in Tab. REF . Given the limited number of clips and low-quality videos from the ReplayAttack dataset, it is hard to learn generalizable features which can perform very well on other datasets, so the error rate in protocol \(I \rightarrow C\)  is still high compared with \(C \rightarrow I\) . Our proposed framework can achieve competitive performance compared to previous works in both protocols.
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